The present invention relates to border detection in medical imaging. For example, vessel borders are detected in medical ultrasound images to determine a vessel width, an intima-media thickness (IMT), the inner or outer boundary of the fetal head, the edge of a bone, heart ventricle, a cyst, or any object imaged that has a distinguishable gradient in the neighborhood of the boundary.
For objective analysis of medical images, the borders of the vessels are detected. In one embodiment, a user inputs the border, such as using a touch screen, track ball or mouse to draw the border based on a displayed image. Automatic or processor based border detection is provided in other embodiments. For example, the intensity of the images is thresholded to identify locations of the vessel wall as opposed to locations within the vessel. As another example, certain identified locations along a border may be used for a curved fitting calculation to represent the border.
One type of algorithm for edge detection is based on Marr's theory. The zero-crossing of the second derivative of the image signals corresponds to the edge of the objects. A filter removes the noise in the image. The second derivative of the filtered image is determined. The zero crossing a points of the second derivative image are identified as potential border pixels. Combining these three steps together, a Marr's theory based edge is defined as the zero-crossings of the Laplacian of the Gaussian operator (i.e., the Gaussian function behaves as a low pass filter) applied to the image for various values of sigma, the standard deviation of the Gaussian function. Whenever no ambiguous connections between independently detected edges are possible, these connected edges determine the boundary.
Another type of algorithm for edge detection is Canny edge detection. Canny edge detection is based on the extreme of the first derivative of the Gaussian operator applied to the image for various values of sigma, the standard deviation of the Gaussian function. Canny methods use two thresholds to link edge points. These edge points are the identified potential boundary points and, when there are no local extremes only the absolute extreme is detected and that is the boundary.
Yet another type of algorithm for edge detection in a deformable model and active contour (e.g., a snake model). The deformable model is a spline or surface with controlled continuity. The energy terms control movement of the points in the model. These energy terms include the internal (i.e., continuity of the model), the external (i.e., image information such as image gradient), and some other constraints set by the operators. By minimizing the energy (usually called cost function), the final position of the contour is defined. This contour is the defined boundary.
Another type of algorithm is the level set method. The level set method treats the active contour as the zero level set of a higher dimensional surface. The entire surface evolves to minimize a metric defined by the image gradient and curvature.
One accepted method for determining a boundary in medical imaging is the deformable model. The essence of this method is to build a cost function and evolve an initial boundary to the final boundary associated with the minimum cost value. Unfortunately, the terms developed for the cost function are more or less subjective.
However, these automatic techniques may have difficulty due to speckle content or other noise within an ultrasound image. Also, an automatic method may be limited to specific types of imaging situations, such as only detecting a border where a cross section or only where a longitudinal view of the vessel is available. Obtaining a specific one of longitudinal or cross-sectional views of particular vessels may be difficult due to limited acoustic windows into the body.